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change js_face_recognition sample with yunet

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Wanli 2024-04-22 15:59:54 +08:00
parent 3f8b56ec49
commit 096ccd410b
1 changed files with 51 additions and 27 deletions
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68
samples/dnn/js_face_recognition.html
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@ -12,27 +12,40 @@ var persons = {};
//! [Run face detection model]
function detectFaces(img) {
var blob = cv.blobFromImage(img, 1, {width: 192, height: 144}, [104, 117, 123, 0], false, false);
netDet.setInput(blob);
var out = netDet.forward();
netDet.setInputSize(new cv.Size(img.cols, img.rows));
var out = new cv.Mat();
netDet.detect(img, out);
var faces = [];
for (var i = 0, n = out.data32F.length; i < n; i += 7) {
var confidence = out.data32F[i + 2];
var left = out.data32F[i + 3] * img.cols;
var top = out.data32F[i + 4] * img.rows;
var right = out.data32F[i + 5] * img.cols;
var bottom = out.data32F[i + 6] * img.rows;
for (var i = 0, n = out.data32F.length; i < n; i += 15) {
var left = out.data32F[i];
var top = out.data32F[i + 1];
var right = (out.data32F[i] + out.data32F[i + 2]);
var bottom = (out.data32F[i + 1] + out.data32F[i + 3]);
left = Math.min(Math.max(0, left), img.cols - 1);
top = Math.min(Math.max(0, top), img.rows - 1);
right = Math.min(Math.max(0, right), img.cols - 1);
bottom = Math.min(Math.max(0, bottom), img.rows - 1);
top = Math.min(Math.max(0, top), img.rows - 1);
if (confidence > 0.5 && left < right && top < bottom) {
faces.push({x: left, y: top, width: right - left, height: bottom - top})
if (left < right && top < bottom) {
faces.push({
x: left,
y: top,
width: right - left,
height: bottom - top,
x1: out.data32F[i + 4] < 0 || out.data32F[i + 4] > img.cols - 1 ? -1 : out.data32F[i + 4],
y1: out.data32F[i + 5] < 0 || out.data32F[i + 5] > img.rows - 1 ? -1 : out.data32F[i + 5],
x2: out.data32F[i + 6] < 0 || out.data32F[i + 6] > img.cols - 1 ? -1 : out.data32F[i + 6],
y2: out.data32F[i + 7] < 0 || out.data32F[i + 7] > img.rows - 1 ? -1 : out.data32F[i + 7],
x3: out.data32F[i + 8] < 0 || out.data32F[i + 8] > img.cols - 1 ? -1 : out.data32F[i + 8],
y3: out.data32F[i + 9] < 0 || out.data32F[i + 9] > img.rows - 1 ? -1 : out.data32F[i + 9],
x4: out.data32F[i + 10] < 0 || out.data32F[i + 10] > img.cols - 1 ? -1 : out.data32F[i + 10],
y4: out.data32F[i + 11] < 0 || out.data32F[i + 11] > img.rows - 1 ? -1 : out.data32F[i + 11],
x5: out.data32F[i + 12] < 0 || out.data32F[i + 12] > img.cols - 1 ? -1 : out.data32F[i + 12],
y5: out.data32F[i + 13] < 0 || out.data32F[i + 13] > img.rows - 1 ? -1 : out.data32F[i + 13],
confidence: out.data32F[i + 14]
})
}
}
blob.delete();
out.delete();
return faces;
};
@ -53,7 +66,7 @@ function recognize(face) {
var vec = face2vec(face);
var bestMatchName = 'unknown';
var bestMatchScore = 0.5; // Actually, the minimum is -1 but we use it as a threshold.
var bestMatchScore = 30; // Threshold for face recognition.
for (name in persons) {
var personVec = persons[name];
var score = vec.dot(personVec);
@ -69,24 +82,25 @@ function recognize(face) {
function loadModels(callback) {
var utils = new Utils('');
var proto = 'https://raw.githubusercontent.com/opencv/opencv/4.x/samples/dnn/face_detector/deploy_lowres.prototxt';
var weights = 'https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel';
var detectModel = 'https://media.githubusercontent.com/media/opencv/opencv_zoo/main/models/face_detection_yunet/face_detection_yunet_2023mar.onnx';
var recognModel = 'https://media.githubusercontent.com/media/opencv/opencv_zoo/main/models/face_recognition_sface/face_recognition_sface_2021dec.onnx';
utils.createFileFromUrl('face_detector.prototxt', proto, () => {
document.getElementById('status').innerHTML = 'Downloading face_detector.caffemodel';
utils.createFileFromUrl('face_detector.caffemodel', weights, () => {
document.getElementById('status').innerHTML = 'Downloading YuNet model';
utils.createFileFromUrl('face_detection_yunet_2023mar.onnx', detectModel, () => {
document.getElementById('status').innerHTML = 'Downloading OpenFace model';
utils.createFileFromUrl('face_recognition_sface_2021dec.onnx', recognModel, () => {
document.getElementById('status').innerHTML = '';
netDet = cv.readNetFromCaffe('face_detector.prototxt', 'face_detector.caffemodel');
netDet = new cv.FaceDetectorYN("face_detection_yunet_2023mar.onnx", "", new cv.Size(320, 320), 0.9, 0.3, 5000);
netRecogn = cv.readNet('face_recognition_sface_2021dec.onnx');
callback();
});
});
});
};
function main() {
if(!cv.FaceDetectorYN){
alert(`Error: This sample require OpenCV.js built with FaceDetectorYN. Please rebuild it with FaceDetectorYN or use the latest version of OpenCV.js.`);
return;
}
// Create a camera object.
var output = document.getElementById('output');
var camera = document.createElement("video");
@ -146,6 +160,16 @@ function main() {
var faces = detectFaces(frameBGR);
faces.forEach(function(rect) {
cv.rectangle(frame, {x: rect.x, y: rect.y}, {x: rect.x + rect.width, y: rect.y + rect.height}, [0, 255, 0, 255]);
if(rect.x1>0 && rect.y1>0)
cv.circle(frame, {x: rect.x1, y: rect.y1}, 2, [255, 0, 0, 255], 2)
if(rect.x2>0 && rect.y2>0)
cv.circle(frame, {x: rect.x2, y: rect.y2}, 2, [0, 0, 255, 255], 2)
if(rect.x3>0 && rect.y3>0)
cv.circle(frame, {x: rect.x3, y: rect.y3}, 2, [0, 255, 0, 255], 2)
if(rect.x4>0 && rect.y4>0)
cv.circle(frame, {x: rect.x4, y: rect.y4}, 2, [255, 0, 255, 255], 2)
if(rect.x5>0 && rect.y5>0)
cv.circle(frame, {x: rect.x5, y: rect.y5}, 2, [0, 255, 255, 255], 2)
var face = frameBGR.roi(rect);
var name = recognize(face);